Q&A: John Sculley
John Sculley is the former CEO of both Apple and Pepsi-Cola, and the current Chairman of PeopleTicker; he’s also a member of the Sourcing Industry Group (SIG) Advisory Board. Outsource was lucky enough to catch up with John at the SIG Summit in Carlsbad, California, and to get treated to some remarkable insight from one of the sharpest minds in business…
Outsource: John, thank you so much for joining us today. It’s genuinely an honour. We have polled a few people for the opening question to this interview and the most popular query is this: what’s the one thing in your career that you’re most proud of?
John Sculley: I think the thing I’m most proud of is that I’m still relevant with people that are making a difference in the world. I’m a mentor to several very talented entrepreneurs who believe they can change the world and are working in fields including health tech, marketing tech, the reinvention of work, regenerative medicine, and it’s all because I have an insatiable curiosity. I’ve always been interested to know why are things the way they are, and isn’t there a better way?
O: That certainly comes across in your book Moonshot which since the last Summit so many of us have read… There seems to be something in you that’s kind of really fascinated with the inner working of things. Is that something that goes back to your childhood?
JS: Growing up I was never really interested in toys. I always wanted to take things apart. I can remember, as young as six years old, all I wanted for Christmas was a battery, some wire and some lights and switches so I could start to put them together and understand how electricity worked. I used to take radios apart and eventually turn receivers into transmitters. I was a ham radio operator from the age of 13, and built my own cathode ray television when I was 14 – so I’ve always been a tinkerer at heart.
O: That seems like the kind of skill that’s most relevant in today’s world – and something you talked about at the last Summit when you did your fireside chat with [SIG CEO] Dawn [Tiura] talking about where work’s going, and what is going to be relevant and sustainable as we go forward as a species. Do you see that as an essential component of your success over what’s been a very long and prolific career?
JS: Well, I think one of the skills I feel is most important for someone with an insatiable curiosity is to be a good listener. When I was at Apple, Steve Jobs said he didn’t believe in normal titles; I was the CEO and he was the chairman, but we all made up our own business cards: I thought about it for a while and I came up with my title of Chief Listener. The longer I’m around the more I value that as a skill. It’s very easy to give a presentation, to give a pitch; it’s a lot harder to listen and observe carefully. One of the professors at MIT that I’ve always respected so much, Marvin Minsky – one of the fathers of artificial intelligence – said that you don’t always really understand something well unless you understand it more than one way. And so a good listener may be observing the same facts as everybody else, but a good listener thinks about those facts and tries to put those into other contexts, into other domains they may have, and sometimes see things in a different way.
O: Is that one of the reasons why you’ve enjoyed getting involved with SIG? Because of the number of different conversations that are going on and perhaps the variety of different listening perspectives that people are exhibiting?
JS: Well I’ve been fascinated for some time with this whole idea that work is going to be reinvented again. I watched work go from the time that I was a CEO at Pepsi where the work innovation was called an electronic word processer; and I went to Apple and a small percentage of the population was beginning to use something called the green-screen personal computer – but it was still a novelty, it wasn’t yet particularly useful, much less indispensible – and we’ve seen for that whole era since I arrived in Silicon Valley all the way back in 1982 to today that every high tech company was focused on creating tools for knowledge workers that would improve productivity. No matter what part of the value chain they’re in. Well, something’s fundamentally changed and that fundamental change is that now the computers are getting smart enough that they talk to each other and so we’re starting to see that the computers, machine to machine, are learning from each other. Sensors are exponentially growing in importance in technology which means by 2020 the estimates are that there will be over 20 billion wireless connected devices. Meanwhile there are only 7 billion people on the planet so most of those are machine to machine, so the machines are getting smarter – and we haven’t really absorbed that yet into what the implications are for work.
The first thing we have seen already and that is that we can no longer understand work by looking at the organisation chart. It isn’t about functional organisations anymore. It’s about projects, and project teams are made up of individuals that are one’s own organisation, full-time employees, increasingly it’s independent contractors being added to that – and they may also be members of customers that are part of the project team, it can be business members that are part of the project team. The project team is a much more flexible way of thinking about work and that’s why we now see in the tech world, that some of the most successful companies that are helping in the reinvention of work are creating collaborative tools. You know it may be techs, it may be access to data in the cloud, it may be creating privatised channels that people can talk to one another regardless of whether or not they’re on the organisational chart in the same function.
It was only a few years ago that we used to say that long-term projects could take ten years. Well now we know that what used to get done in ten years needs to get done in three years. What used to get done in three years needs to get done in three months. So what’s the derivative affect on work? The derivative effect is certainly that workforces need to be more flexible, they need to be able to be assembled for the purpose and they must be incredibly cost effective for the purpose. So that’s a power shift to the importance of the independent contractor, the importance of work being done wherever it can be done most effectively and most efficiently. It’s a dramatic change in how we think about workforces, how we think what work humans can do, what can be offloaded to technology and how they may complement each other – and even be in conflict from time to time.
O: Obviously there are huge implications for the individual worker, and one is that there will inevitably be more time out of work between engagements for many people. Do you feel that some kind of basic universal income is a necessity?
JS: Well I first heard about the universal basic income just a few years ago and I wasn’t really taking it particularly seriously but the more that I’m involved in artificial intelligence (and machine learning happens to be one of the areas that I’m quite deeply into) the more I realise that for a lot of today’s skilled jobs – accountants, lawyers, pharmacologists, radiologists – a large part of what those jobs define can be done cheaper, better, faster by machines. So what’s the implication on skilled work? We saw how the muscle work moved to robots. Now we’re seeing some of the brainwork is going to go to machines. Then the question is, what does it mean in terms of economies? You can’t have a growing economy if you don’t have people there who have the ability to spend. So there are huge societal issues that we don’t really fully know how to cope with. In developed countries, we can make the pragmatic shifts over time to better education especially around vocational skills – see what Germany has had for years with an apprentice-based system, which has done them well. That is less developed in certain other counties including my own here in the States.
I think the reality is, that the estimate is that by 2030, 60% of the skilled jobs are ones that don’t yet exist today. We’re going to see entirely new jobs. Now will there be enough to fill the requirements of everybody that wants a job? I don’t know the answer to that – and maybe a universal basic income (which is kind of an idea taken less seriously or more seriously depending on who you talk to) will have more context over the next 15 years – but I do believe that the whole concept of how you get work done is going to fundamentally change, and the trend is already apparent. It’s shifting more and more towards people working for themselves, people having more than one job at a time, meaning that if you’re a project executive, you may work full time on a project for some period of time, and then the project is completed and you find something else to do, or you may pass your time on multiple projects, on multiple project teams at the same time. Either of those models, I think is very realistic. The question is, will you have the right skills?
Another question is, I was reading recently that Cisco has an institute that is looking out to what kind of future can we expect in the terms of technology, and is projecting that by 2019, 86% of work will require, in some way, cloud computing. Well, that’s not where we’re at today. And so you say, how is that possible to go from today, to 2019? Well if you think in linear time it’s not realistic. Linear time is what most of us intuitively have grown up with. It’s what Kodak was thinking back in 2007 when it doubled down on traditional photography because it was in competition with Wal-Mart in the States for market share of the single-use film camera. Kodak invented the digital camera. Kodak was totally familiar with Photoshop and digital printing – they were in the printing business – but they doubled down on the traditional single-use camera. That was the same year that Steve Jobs at Apple launched the first iPhone. Kodak saw the importance of digital photography but they saw in linear time. Highly trained engineers recruited from the best technical universities in the world – so these were smart people – thought in linear time. So they said yes this will happen someday but not soon. The reality was we were into exponential time, a logarithmic scale of growth. And so by 2010, three years later, Apple had redefined how we saw the world with the iPhone, and then the smartphones from Android and other related inventions became fundamental to us. We went from curiosity to useful to indispensible, in exponential time – and what happened to Kodak? The 20-billion-dollar Kodak filed for bankruptcy.
This is what’s difficult for people to understand with the reinvention of work. This is a nonlinear change in society – and nonlinear changes are difficult for us to absorb as humans. It touches our personal lives as well as business lives and so a conference like SIG is incredibly important to bring leaders together in an industry so that there can be conversation potentially even leading to collaboration. To say, “Wait a minute, we don’t live in anymore in linear time, we live in exponential time. You know so, how can we learn from each other? What can we do to accelerate the adaption of new ways of thinking?” And the one thing I can assure anyone who is interested in this subject is that data is going to be important. So let’s take, for example, the way we think about recruiting, the procurement of new talent, within a flexible workforce where you want the right skills in the right place in the right time. How can you possibly do that by the seat of your pants intuition anymore? Someone may have 30 years’ experience of intuition, they may be uniquely talented in making good decision in things, but when you scale this to the US today, when about 10% of the workforce are skilled independent contractors, how can you do that by the seat of your pants? You’ve got to have data. And you have to have data that verifies. Who is the person that you’re considering to be brought in as an independent contractor? What do you pay them? How do you know you aren’t paying too much for that particular job? How do you plan where work should go to get done? Should work be done in some locations? Or should it be done in other locations? You know, how do you make the decision of how much of that project team should be full-time employees versus people you bring in as independent contractors if you don’t know the cost? So these should be examples of the importance of data.
More and more jobs particularly in the independent contractor world require licenses: you have to have people with verified skills. In many countries it requires that for certain tasks they have to have a verified license. So, again, one more example of the importance of data. Procurement officers are a real high-profile role, in many cases it’s even a C-suite job; how do you begin to equip organisations, and leadership organisations, to begin to use data more effectively – because at scale, you’re talking about potentially gigantic cost implications, either spending too much or saving a lot, depending on how well you use your data tools. It’s like when I was COO of Pepsi, we didn’t have personal computers and spreadsheets, and yet we now take that for granted – we have for 30+ years – so what are going to be the digital tools that we are going to take for granted in the procurement of talent in the future that will include machine learning, that are going to include unstructured data analytics, that are going to include domain expertise in different parts of the work process? That’s going to be so obvious to someone that’s in the workforce five years, ten years from now so if it’s going to be obvious five to ten years from now, it better start getting obvious to those of us who are in leadership positions today.
O: Was it thinking like that that made you get involved with PeopleTicker in the first place?
JS: Well, I had no part in the founding of PeopleTicker, but [CEO] Joe Musacchio explained to me what he was doing and I do have a fair amount of experience in data science; then I became confident the quality of data was actually incredibly representative of what was actually happening out in the marketplace and I saw that the data could be tracking, in the case of PeopleTicker, 16 different industries, capturing data in real time, every city in the US, every level of job within those 16 different industries, all focused on skills and very simple objectives. What is the pay rate for every one of those jobs? And then starting to look at the data, and seeing that the pay rate does vary from location to location and the pay rates that people were paying for the jobs were often very different than what the true pay rate was for what those jobs were for in the market – and then when you looked at companies that were using that pay rate data starting to see that some had been way overpaying based on what the jobs were actually worth in the marketplace… Then I saw that Joe was expanding that. First in 10 countries, then to 40, then eventually he wants to get to 100 countries. Well as you know, work gets done in anywhere in the world so, think of a spreadsheet. A spreadsheet we know is a planning tool; well with People Ticker, you can do a spreadsheet as a planning tool of where should work get done, what’s the mix of full time employees versus contractors. How should I think about work as offshored through other parts of the world versus what work should be done in my home country? You know, all of those things can be done as easily as working with a traditional spreadsheet.
O: You’ve gone through some incredible waves of change during your career – and there are certainly more coming. What are you most excited about? What makes you smile?
JS: I’m a big believer that as we harness the power of smart machines we can help the world be an even better place. Many people have said, what about unintended consequences? Will the machines eventually get so smart that they will take control of our lives? Will the machines get so smart that the smart machines get into the wrong hands? All of those things are possibilities. But there are all kinds of possibilities about any subject. What I’m interested in is saying how can we use smart machines to help us make better decisions about how we live in our global environment? How can we use smart machines to help create more potable water? There’s not enough water that’s drinkable, human quality around the world. Literally billions of people are drinking substandard water. How can we use smart machines for regenerative medicine so that we can actually go in and take stem cells for example and be able to regenerate people’s aging tissues and not only extend life but be able to extend the quality of life? How can we solve many serious diseases – cancer probably being the highest profile one right now – working at the genomics level? How can we use one of the biggest breakthroughs in science since Crick and Watson discovered the double helix DNA in 1953: CRISPR, the gene editing tool? For the first time, it’s not just about discovering DNA, it’s not just about doing a human genome sequencing less expensively: it actually means that we can go in and edit DNA that may need corrections. It also means incredible changes in material science. The ability to think about non-fossil fuel and alternatives that can be created from reprograming a yeast cell. All of these things are becoming totally realistic. And it’s all happening in exponential time.
What will be possible in 15 years is hard for us to get our heads around, because of exponential time. I am confident that in my lifetime, the most amazing and inspiring changes are still yet to happen. I’ve been fortunate enough to have seen many changes already, going back to that little kid that I was, building my first transmitter, back in the 1950s; I’ve seen decades and decades of incredible change; and yet I’m very confident that I’m going to see in the rest of my life more changes, and more important changes, to the world and to society, than have ever yet taken place. And that’s because we live in an era of exponential time and multiple technologies evolving. It’s not just Moore’s law; that is a progression around a pretty singular path. It’s about what’s happening with computation; what’s happening with storage, with mobility, with Internet of Things, with precision medicine – and these are all happening in parallel, and they all intersect at different times. So what does that mean for work? It means that the jobs of the future, whatever they are, will be different from the jobs of today.